In contrast, we show that on-policy approaches that leverage interaction with the demonstrator [1]} or take advantage of interaction with the environment (in the style of Inverse Optimal Control [2]}) [3]}, [4]} are always (i.e. independent of parameters) asymptotically consistent on these problems. We believe this strong separation between on- and off- policy approaches helps explain both the poor performance of behavioral cloning even in regimes with large data and powerful model classes [5]}, [6]}, [7]}, [8]}, [9]}, [10]} and the success of on-policy methods mentioned above.
